Bibliografias temáticas / Annotations (Provenance)

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Literatura científica selecionada sobre o tema "Annotations (Provenance)"

Autor: Grafiati

Publicado: 25 de julho de 2024

Última modificação: 31 de julho de 2024

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Artigos de revistas sobre o assunto "Annotations (Provenance)"

Wang, Liwei, Henning Koehler, Ke Deng, Xiaofang Zhou e Shazia Sadiq. "Flexible Provenance Tracing". International Journal of Systems and Service-Oriented Engineering 2, n.º 2 (abril de 2011): 1–20. http://dx.doi.org/10.4018/jssoe.2011040101.

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The description of the origins of a piece of data and the transformations by which it arrived in a database is termed the data provenance. The importance of data provenance has already been widely recognized in database community. The two major approaches to representing provenance information use annotations and inversion. While annotation is metadata pre-computed to include the derivation history of a data product, the inversion method finds the source data based on the situation that some derivation process can be inverted. Annotations are flexible to represent diverse provenance metadata but the complete provenance data may outsize data itself. Inversion method is concise by using a single inverse query or function but the provenance needs to be computed on-the-fly. This paper proposes a new provenance representation which is a hybrid of annotation and inversion methods in order to achieve combined advantage. This representation is adaptive to the storage constraint and the response time requirement of provenance inversion on-the-fly.

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Bourgaux, Camille, e Ana Ozaki. "Querying Attributed DL-Lite Ontologies Using Provenance Semirings". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julho de 2019): 2719–26. http://dx.doi.org/10.1609/aaai.v33i01.33012719.

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Attributed description logic is a recently proposed formalism, targeted for graph-based representation formats, which enriches description logic concepts and roles with finite sets of attribute-value pairs, called annotations. One of the most important uses of annotations is to record provenance information. In this work, we first investigate the complexity of satisfiability and query answering for attributed DL-LiteR ontologies. We then propose a new semantics, based on provenance semirings, for integrating provenance information with query answering. Finally, we establish complexity results for satisfiability and query answering under this semantics.

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Haochen Zou, Haochen Zou, Dejian Wang Haochen Zou e Yang Xiao Dejian Wang. "Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data". 電腦學刊 34, n.º 2 (abril de 2023): 081–97. http://dx.doi.org/10.53106/199115992023043402007.

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<p>Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.</p> <p> </p>

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Nadendla, Suvarna, Rebecca Jackson, James Munro, Federica Quaglia, Bálint Mészáros, Dustin Olley, Elizabeth T. Hobbs et al. "ECO: the Evidence and Conclusion Ontology, an update for 2022". Nucleic Acids Research 50, n.º D1 (19 de novembro de 2021): D1515—D1521. http://dx.doi.org/10.1093/nar/gkab1025.

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Abstract The Evidence and Conclusion Ontology (ECO) is a community resource that provides an ontology of terms used to capture the type of evidence that supports biomedical annotations and assertions. Consistent capture of evidence information with ECO allows tracking of annotation provenance, establishment of quality control measures, and evidence-based data mining. ECO is in use by dozens of data repositories and resources with both specific and general areas of focus. ECO is continually being expanded and enhanced in response to user requests as well as our aim to adhere to community best-practices for ontology development. The ECO support team engages in multiple collaborations with other ontologies and annotating groups. Here we report on recent updates to the ECO ontology itself as well as associated resources that are available through this project. ECO project products are freely available for download from the project website (https://evidenceontology.org/) and GitHub (https://github.com/evidenceontology/evidenceontology). ECO is released into the public domain under a CC0 1.0 Universal license.

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Martin, Chris J., Mohammed H. Haji, Peter K. Jimack, Michael J. Pilling e Peter M. Dew. "A user-orientated approach to provenance capture and representation for in silico experiments, explored within the atmospheric chemistry community". Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, n.º 1898 (13 de julho de 2009): 2753–70. http://dx.doi.org/10.1098/rsta.2009.0044.

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We present a novel user-orientated approach to provenance capture and representation for in silico experiments, contrasted against the more systems-orientated approaches that have been typical within the e-Science domain. In our approach, we seek to capture the scientist's reasoning in the form of annotations as an experiment evolves, while using the scientist's terminology in the representation of process provenance. Our user-orientated approach is applied in a case study within the atmospheric chemistry domain: we consider the design, development and evaluation of an electronic laboratory notebook, a provenance capture and storage tool, for iterative model development.

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Pei, Jisheng, e Xiaojun Ye. "Information-Balance-Aware Approximated Summarization of Data Provenance". Scientific Programming 2017 (12 de setembro de 2017): 1–11. http://dx.doi.org/10.1155/2017/4504589.

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Extracting useful knowledge from data provenance information has been challenging because provenance information is often overwhelmingly enormous for users to understand. Recently, it has been proposed that we may summarize data provenance items by grouping semantically related provenance annotations so as to achieve concise provenance representation. Users may provide their intended use of the provenance data in terms of provisioning, and the quality of provenance summarization could be optimized for smaller size and closer distance between the provisioning results derived from the summarization and those from the original provenance. However, apart from the intended provisioning use, we notice that more dedicated and diverse user requirements can be expressed and considered in the summarization process by assigning importance weights to provenance elements. Moreover, we introduce information balance index (IBI), an entropy based measurement, to dynamically evaluate the amount of information retained by the summary to check how it suits user requirements. An alternative provenance summarization algorithm that supports manipulation of information balance is presented. Case studies and experiments show that, in summarization process, information balance can be effectively steered towards user-defined goals and requirement-driven variants of the provenance summarizations can be achieved to support a series of interesting scenarios.

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Stork, Lise, Andreas Weber, Eulàlia Miracle e Katherine Wolstencroft. "A Workflow for the Semantic Annotation of Field Books and Specimen Labels". Biodiversity Information Science and Standards 2 (13 de junho de 2018): e25839. http://dx.doi.org/10.3897/biss.2.25839.

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Geographical and taxonomical referencing of specimens and documented species observations from within and across natural history collections is vital for ongoing species research. However, much of the historical data such as field books, diaries and specimens, are challenging to work with. They are computationally inaccessable, refer to historical place names and taxonomies, and are written in a variety of languages. In order to address these challenges and elucidate historical species observation data, we developed a workflow to (i) crowd-source semantic annotations from handwritten species observations, (ii) transform them into RDF (Resource Description Framework) and (iii) store and link them in a knowledge base. Instead of full-transcription we directly annotate digital field books scans with key concepts that are based on Darwin Core standards. Our workflow stresses the importance of verbatim annotation. The interpretation of the historical content, such a resolving a historical taxon to a current one, can be done by individual researchers after the content is published as linked open data. Through the storage of annotion provenance, who created the annotation and when, we allow multiple interpretations of the content to exist in parallel, stimulating scientific discourse. The semantic annotation process is supported by a web application, the Semantic Field Book (SFB)-Annotator, driven by an application ontology. The ontology formally describes the content and meta-data required to semantically annotate species observations. It is based on the Darwin Core standard (DwC), Uberon and the Geonames ontology. The provenance of annotations is stored using the Web Annotation Data Model. Adhering to the principles of FAIR (Findable, Accessible, Interoperable & Reusable) and Linked Open Data, the content of the specimen collections can be interpreted homogeneously and aggregated across datasets. This work is part of the Making Sense project: makingsenseproject.org. The project aims to disclose the content of a natural history collection: a 17,000 page account of the exploration of the Indonesian Archipelago between 1820 and 1850 (Natuurkundige Commissie voor Nederlands-Indie) With a knowledge base, researchers are given easy access to the primary sources of natural history collections. For their research, they can aggregate species observations, construct rich queries to browse through the data and add their own interpretations regarding the meaning of the historical content.

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Bonatti, Piero A., Aidan Hogan, Axel Polleres e Luigi Sauro. "Robust and scalable Linked Data reasoning incorporating provenance and trust annotations". Journal of Web Semantics 9, n.º 2 (julho de 2011): 165–201. http://dx.doi.org/10.1016/j.websem.2011.06.003.

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Hernández, Daniel, Luis Galárraga e Katja Hose. "Computing how-provenance for SPARQL queries via query rewriting". Proceedings of the VLDB Endowment 14, n.º 13 (setembro de 2021): 3389–401. http://dx.doi.org/10.14778/3484224.3484235.

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Over the past few years, we have witnessed the emergence of large knowledge graphs built by extracting and combining information from multiple sources. This has propelled many advances in query processing over knowledge graphs, however the aspect of providing provenance explanations for query results has so far been mostly neglected. We therefore propose a novel method, SPARQLprov, based on query rewriting, to compute how-provenance polynomials for SPARQL queries over knowledge graphs. Contrary to existing works, SPARQLprov is system-agnostic and can be applied to standard and already deployed SPARQL engines without the need of customized extensions. We rely on spm-semirings to compute polynomial annotations that respect the property of commutation with homomorphisms on monotonic and non-monotonic SPARQL queries without aggregate functions. Our evaluation on real and synthetic data shows that SPARQLprov over standard engines incurs an acceptable runtime overhead w.r.t. the original query, competing with state-of-the-art solutions for how-provenance computation.

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MORAES, PEDRO LUÍS RODRIGUES DE. "Miscellaneous notes on specimens collected in Surat, Johanna Island and Cácota de Suratá, with emphasis on the Linnaean Herbarium (LINN)". Phytotaxa 603, n.º 1 (17 de julho de 2023): 43–59. http://dx.doi.org/10.11646/phytotaxa.603.1.3.

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The aim of this paper was to investigate whether specimens in the Linnaean Herbarium (LINN), with Linnaean annotations of provenance as from Suratte/Suratt or Johanna Island, would be original material for their respective species names. As Linnaeus did not explicitly cite a specimen in the protolog of those names, neither the collector for most of them, the possible origin of each individual specimen was checked and searched from independent evidence, such as an explicit annotation, or a link to a dated list or letter among Linnaeus’s surviving correspondence, or from Linnaeus’s Paper Slips. Regarding the typification of those names, published choices of type have been found, but on a careful evaluation some were found to be ineffective or supersedable. This was the case for Cenchrus muricatus L. and Utricularia stellaris L.f., whose lectotypifications are proposed here.

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Teses / dissertações sobre o assunto "Annotations (Provenance)"

Bell, Michael James. "Provenance, propagation and quality of biological annotation". Thesis, University of Newcastle upon Tyne, 2014. http://hdl.handle.net/10443/2774.

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Biological databases have become an integral part of the life sciences, being used to store, organise and share ever-increasing quantities and types of data. Biological databases are typically centred around raw data, with individual entries being assigned to a single piece of biological data, such as a DNA sequence. Although essential, a reader can obtain little information from the raw data alone. Therefore, many databases aim to supplement their entries with annotation, allowing the current knowledge about the underlying data to be conveyed to a reader. Although annotations come in many different forms, most databases provide some form of free text annotation. Given that annotations can form the foundations of future work, it is important that a user is able to evaluate the quality and correctness of an annotation. However, this is rarely straightforward. The amount of annotation, and the way in which it is curated, varies between databases. For example, the production of an annotation in some databases is entirely automated, without any manual intervention. Further, sections of annotations may be reused, being propagated between entries and, potentially, external databases. This provenance and curation information is not always apparent to a user. The work described within this thesis explores issues relating to biological annotation quality. While the most valuable annotation is often contained within free text, its lack of structure makes it hard to assess. Initially, this work describes a generic approach that allows textual annotations to be quantitatively measured. This approach is based upon the application of Zipf's Law to words within textual annotation, resulting in a single value. The relationship between the value and Zipf's principle of least effort provides an indication as to the annotations quality, whilst also allowing annotations to be quantitatively compared. Secondly, the thesis focuses on determining annotation provenance and tracking any subsequent propagation. This is achieved through the development of a visualisation - i - framework, which exploits the reuse of sentences within annotations. Utilising this framework a number of propagation patterns were identified, which on analysis appear to indicate low quality and erroneous annotation. Together, these approaches increase our understanding in the textual characteristics of biological annotation, and suggests that this understanding can be used to increase the overall quality of these resources.

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Livros sobre o assunto "Annotations (Provenance)"

Moreau, Luc, e Ian Foster, eds. Provenance and Annotation of Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850.

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Glavic, Boris, Vanessa Braganholo e David Koop, eds. Provenance and Annotation of Data and Processes. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80960-7.

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Ludäscher, Bertram, e Beth Plale, eds. Provenance and Annotation of Data and Processes. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16462-5.

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Groth, Paul, e James Frew, eds. Provenance and Annotation of Data and Processes. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34222-6.

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Belhajjame, Khalid, Ashish Gehani e Pinar Alper, eds. Provenance and Annotation of Data and Processes. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98379-0.

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Mattoso, Marta, e Boris Glavic, eds. Provenance and Annotation of Data and Processes. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40593-3.

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McGuinness, Deborah L., James R. Michaelis e Luc Moreau, eds. Provenance and Annotation of Data and Processes. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17819-1.

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Freire, Juliana, David Koop e Luc Moreau, eds. Provenance and Annotation of Data and Processes. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89965-5.

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McGuinness, Deborah L. Provenance and Annotation of Data and Processes: Third International Provenance and Annotation Workshop, IPAW 2010, Troy, NY, USA, June 15-16, 2010. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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International Provenance and Annotation Workshop (2nd 2008 Salt Lake City, Utah). Provenance and annotation of data and processes: Second International Provenance and Annotation Workshop, IPAW 2008, Salt Lake City, UT, USA, June 17-18, 2008 : revised selected papers. Berlin: Springer, 2008.

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Capítulos de livros sobre o assunto "Annotations (Provenance)"

Kostovska, Ana, Sašo Džeroski e Panče Panov. "Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schema". In Discovery Science, 140–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61527-7_10.

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Abstract With the pervasiveness of data mining (DM) in many areas of our society, the management of digital data, readily available for analysis, has become increasingly important. Consequently, nearly all community accepted guidelines and principles (e.g. FAIR and TRUST) for publishing such data in the digital ecosystem, stress the importance of semantic data enhancement. Having rich semantic annotation of DM datasets would support the data mining process at various choice points, such as data understanding, automatic identification of the analysis task, and reasoning over the obtained results. In this paper, we report on the developments of an ontology-based annotation schema for semantic description of DM datasets. The annotation schema combines three different aspects of semantic annotation, i.e., annotation of provenance, data mining specific, and domain-specific information. We demonstrate the utility of these annotations in two use cases: semantic annotation of remote sensing data and data about neurodegenerative diseases.

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Tikat, Maroua, Aline Menin, Michel Buffa e Marco Winckler. "Engineering Annotations to Support Analytical Provenance in Visual Exploration Processes". In Lecture Notes in Computer Science, 204–18. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09917-5_14.

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Del Rio, Mauro, Luca Lianas, Oskar Aspegren, Giovanni Busonera, Francesco Versaci, Renata Zelic, Per H. Vincent et al. "AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies". In Lecture Notes in Computer Science, 545–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13321-3_48.

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AbstractWhile studies in pathology are essential for the progress in the diagnostic and prognostic techniques in the field, pathologist time is becoming an increasingly scarce resource, and can indeed become the limiting factor in the feasibility of studies to be performed. In this work, we demonstrate how the Digital Pathology platform by CRS4, for supporting research studies in digital pathology, has been augmented by the addition of AI-based features to accelerate image examination to reduce the pathologist time required for clinical studies. The platform has been extended to provide computationally generated annotations and visual cues to help the pathologist prioritize high-interest image areas. The system includes an image annotation pipeline with DeepHealth-based deep learning models for tissue identification and prostate cancer identification. Annotations are viewed through the platform’s virtual microscope and can be controlled interactively (e.g., thresholding, coloring). Moreover, the platform captures inference provenance information and archives it as RO-Crate artifacts containing data and metadata required for reproducibility. We evaluate the models and the inference pipeline, achieving AUC of 0.986 and 0.969 for tissue and cancer identification, respectively, and verifying linear dependence of execution speed on image tissue content. Finally, we describe the ongoing clinical validation of the contribution, including preliminary results, and discuss feedback from clinical professionals regarding the overall approach.

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Chen, Liming, Zhuoan Jiao e Simon J. Cox. "On the Use of Semantic Annotations for Supporting Provenance in Grids". In Euro-Par 2006 Parallel Processing, 371–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823285_38.

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Dvořák, František, Daniel Kouřil, Aleš Křenek, Luděk Matyska, Miloš Mulač, Jan Pospíšil, Miroslav Ruda, Zdeněk Salvet, Jiří Sitera e Michal Voců. "gLite Job Provenance". In Provenance and Annotation of Data, 246–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_25.

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Barga, Roger S., e Luciano A. Digiampietri. "Automatic Generation of Workflow Provenance". In Provenance and Annotation of Data, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_1.

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Halaschek-Wiener, Christian, Jennifer Golbeck, Andrew Schain, Michael Grove, Bijan Parsia e Jim Hendler. "Annotation and Provenance Tracking in Semantic Web Photo Libraries". In Provenance and Annotation of Data, 82–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_10.

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Gil, Yolanda, Varun Ratnakar e Ewa Deelman. "Metadata Catalogs with Semantic Representations". In Provenance and Annotation of Data, 90–100. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_11.

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Golbeck, Jennifer. "Combining Provenance with Trust in Social Networks for Semantic Web Content Filtering". In Provenance and Annotation of Data, 101–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_12.

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Wootten, Ian, Omer Rana e Shrija Rajbhandari. "Recording Actor State in Scientific Workflows". In Provenance and Annotation of Data, 109–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11890850_13.

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Trabalhos de conferências sobre o assunto "Annotations (Provenance)"

Bourgaux, Camille, Ana Ozaki, Rafael Penaloza e Livia Predoiu. "Provenance for the Description Logic ELHr". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/258.

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We address the problem of handling provenance information in ELHr ontologies. We consider a setting recently introduced for ontology-based data access, based on semirings and extending classical data provenance, in which ontology axioms are annotated with provenance tokens. A consequence inherits the provenance of the axioms involved in deriving it, yielding a provenance polynomial as an annotation. We analyse the semantics for the ELHr case and show that the presence of conjunctions poses various difficulties for handling provenance, some of which are mitigated by assuming multiplicative idempotency of the semiring. Under this assumption, we study three problems: ontology completion with provenance, computing the set of relevant axioms for a consequence, and query answering.

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Xu, Guoyan, Zhijian Wang e Li Yang. "Tracking Framework of Data Provenance Based on Semantic Annotation". In 2012 International Conference on Computer Science and Service System (CSSS). IEEE, 2012. http://dx.doi.org/10.1109/csss.2012.108.

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Xu, Guoyan, Zhijian Wang, Li Yang e Xiaoyi Sun. "Research of Data Provenance Semantic Annotation for Dependency Analysis". In 1st International Conference on Advanced Cloud and Big Data (CBD 2013). IEEE, 2013. http://dx.doi.org/10.1109/cbd.2013.29.

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Bajpai, Amit Kumar, Rajiv Pandey e Surya Prakash Tripathi. "A Semantic Annotation Scheme for Mapping Provenance in Educational Ontology". In 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2021. http://dx.doi.org/10.1109/csnt51715.2021.9509610.

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Ma, Ran, e Yongli Wang. "A Multi-Granularity Annotation Management and Multi-Scale Provenance Compression Technique for Data Streams". In 2009 First International Conference on Information Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.74.

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Temmermans, Frederik, Deepayan Bhowmik, Fernando Pereira e Touradj Ebrahimi. "JPEG Fake Media: a provenance-based sustainable approach to secure and trustworthy media annotation". In Applications of Digital Image Processing XLIV, editado por Andrew G. Tescher e Touradj Ebrahimi. SPIE, 2021. http://dx.doi.org/10.1117/12.2597556.

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Navarrete, Rosa, Diana Martinez- Mosquera, Lorena Recalde e Marco Aguirre. "Evaluating embedded semantics for accessibility description of web crawl data". In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003774.

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The Web is ever expanding, even more by the need for content consumption derived from the pandemic. This fact highlights the need for equity in access to Web content by all people, regardless of their disabilities. To this end, it is essential to focus on web accessibility issues. The World Wide Web Consortium (W3C), the leading organization responsible for ensuring the growth of the social value of the Web, establishes standards, protocols, and recommendations to improve the reach extent of web content for people. For instance, Web Content Accessibility Guidelines (WCAG) promote the achievement of web accessibility. Furthermore, other W3C recommendations foster embedded semantic into the web content to help browsers build a machine-readable data structure aiming to produce an enriched description in search results supporting people to find the right content for their queries and, consequently, improving user experience. Searching for specific web content is especially striving for people with disabilities because they could be forced to explore many search results before finding some content that matches their accessibility requirements. If embedded semantic communicate the accessibility properties of the content, the search will be more productive for everyone but even more for people with special needs. For embedded semantic, two components are required, a vocabulary and an encoding format. Schema.org vocabulary has experienced high growth and encompasses plenty of descriptors for each type of web information, including the set of descriptors for accessibility conditions information. Regarding the format, JSON-LD is the latest W3C recommendation for encoding due to its ability to make JSON data interoperate at Web-scale. It provides a quickly transforming for Linked Data format and is simple enough to be read and written by people. This research conducts a quantitative analysis of the embedded semantic into the web content by processing a dataset obtained from millions of web crawl data for 2021. The data arrive from distinct provenance and purposes at a global scale. In this web content, each annotation is made through script JSON-LD of embedded semantic with Schema's vocabulary. The analysis defines how the accessibility descriptors are used in conjunction with other classes and properties to describe the web information on personal blogs, organizations, events, educational content, universities, persons, commerce, sports, medicine, entertainment, and more. The results provide a perspective of the awareness for accessibility in the different purposes of the Web.The processing was performed on collected zip files that contain over three hundred million records. This analysis was conducted using massive data analysis techniques such as key-value modeling with Python for processing and a NoSQL database such as MongoDB for storage. A new dataset with normalized data was generated with information about domains, types of web content, and properties associated with the accessibility descriptor. The collection and storage layers were implemented on a computing platform with 30GB of RAM, 10 CPUs, and 2TB of storage.This research delivers two main contributions. Firstly, the analysis of the interest in the Web for using accessibility descriptors in embedded semantic. The quantitative results enable us to appreciate the concern about equity and inclusion made visible through accessibility issues in different entities, according to the web domains. Moreover, these results reveal how the W3C recommendation of embedded semantic is being adopted to create a more organized and better-documented Web. Second, processing the raw dataset result in a new normalized dataset in JSON format with information about domains, web content types, and properties associated with the accessibility descriptor. This new dataset will be available for further analysis of the embedded semantic.

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